Since it is hard to establish relations between entities E, which are domain dependent, we try to approximate these relations by using logical flow of Q and A, expressible in domain-independent terms, such as rhetorical relation.

hence we require a certain correspondence between DT-Q and DT-A, considering additional labels for DT nodes by entities (we denote such DT as EDT): (2) a common case is that some entities E are not explicitly mentioned in Q but instead are assumed.


In the default search, munro is missing.

by trying to rhetorically connect munro with the entities in the question, the Virtual DT approach finds out that Munro is an Build discourse tree for the answer DT-Ac.


The contribution of virtual DTs might be insignificant for simpler, shorter, factoid questions when traditional measures of similarity between Q and A work well.

we demonstrate that involvement of background knowledge via virtual DTs for complex convergent questions requiring entailment is significant.


contrast (RightToLeft) attribution (RightToLeft) TEXT: The report indicates joint TEXT: where the missile was fired from elaboration (LeftToRight) <what else does report indicate?> TEXT: and identifies TEXT: who was in control and pins the downing of the plane on the pro-Russian rebels .

elaboration (LeftToRight) attribution (RightToLeft) TEXT: , the Investigative Committee of the Russian Federation believes elaboration (LeftToRight) TEXT: that the plane was hit by a missile from the air <where was it produced?> TEXT: which was not produced in Russia .


The expectation is that a user's biography offers an important source of demographic data.

twitter users' biography is left empty for 48% of users, and others do not supply good-quality information (Culotta et al., 2016).


However, this might even be a difficult task for experts, as there are non-trivial sentence constructions that would need long guidelines to create sentences with exactly one proposition.

our guidelines insured that sentences were reduced in comparison to the original version, if possible.


20 sentences in the random sample were categorized as being ORIGINALSIM-PLE.

some workers still tried to reduce some of these sentences -2 of them were grammatically incorrect (GRAMMAR) and 3 fell into the class INFERENCE.


Additionally, it is not bound to a clearly defined theory (as there is no clearly defined theory on propositions).

it is the representation that is needed to extract information from reviews, as it would help to reduce redundancies, e.g.


On the one hand there are sub-issues in clause splitting.

there are issues besides clause splitting.


Furthermore, we believe that the performance of the original complex sentences, without the preliminary reduction step, would pose an even bigger problem to proposition systems, which implies that using these systems on real data could be problematic.

our study also showed that the ranking of systems is similar among simple and complex sentences.


Also, our intelligent baseline system, that was able to extract verbs, outperformed three of the systems.

the better systems did not only perform much better, but they were also more affected by sentence complexity.


topics of tweets or characteristic elements of the domain jargon).

the result in the cross-domain model scenario, see Table 3 and Table 4, show a different picture.


The similarity is measured in two ways, on the one hand an erroneous form will share a similarity in meaning with the correct spelling as they are realizations of the same word.

an erroneous form is bound to share similarity on the level of characters, as noted by Hill and Hengchen (2019) in their study of OCR typically failing on a few characters on the corpus at hand.


6 If the word exists in the OED, it is spelled correctly.

as we are comparing to the OED lemmas, inflectional forms would be considered as errors, therefore, we lemmatize the word with spaCy 7 (Honnibal and Montani, 2017).


(2018), whose results indicate that combining different methods in normalization can be beneficial, we can indeed get a minor boost for the results of the highest accuracy NMT model if we first try to correct with the above described Word2Vec method and then with NMT, we can increase the overall accuracy to 59.5%.

there is no increase if we invert the order and try to first correct with the NMT and after that with the Word2Vec model.


We found no statistically significant difference in these numbers.

in the Hindi-to-English translation task, HENS drops terms more than twice as often as HEPS (86 versus 38).


A shared characteristic between all of them is the form of the input sequences: a single sentence or a pair of sentences separated by the [SEP] special token, and a classification token ([CLS]) added at the beginning of each example.

the input for multiple-choice reading comprehension questions is assembled by three sentence pieces, i.e., context passage, question, and possible answer(s).


Also, the average question length is 15.7, which is longer compared to the RACE dataset with 10.0.

the option lengths per subject category in our dataset follow a narrower distribution.


(2016) have shown that a simple but supervised word averaging model of sentence embeddings leads to better performance on the paraphrase pairs data set (PPDB).

the performance of their approach is closely related to the supervision from the date set, while without supervision, their approach did not perform well on textual similarity tasks.


Since the Capsule Networks are very new to the field, they have not been used much in NLP tasks.

their good performance in image classification tasks motivated us to use them in offensive language detection tasks too.


As Ziph's law states that most of the words in vocabulary rarely occur in the corpus (Powers, 1998) these methods have some limitation for word pairs who do not co-occur in a context.

according to the distributional hypothesis which states "words that occur in similar contexts tend to have similar meanings" (Harris, 1954), distributional approaches try to recognize the relation between words based on their separate occurrence in the corpus which can be represented for example by their word embedding vectors (Mikolov et al.,2013) and these methods have shown great performance (Baroni et al., 2012;Turney and Pantel, 2010;Roller et al., 2014).


The latter uses a large amount of unlabeled data for abstracting aspects via the statistical topic modeling LDA (Blei et al., 2003) or the aspect-based autoencoder model (He et al., 2017a).

these unsupervised techniques have limitations.


(2015) rank sentences in a parsing tree via a recursive neural network.

the model requires handcrafted features as input.


In other words, these attention signals are applied to all gates of a LSTM cell.

we assume emphasized input makes the cell forget more information on the previous state (the forget gate's function) while this state stores the most salient information by the support of attention signals.


We expect that aspect signals support to generate an informative summary, which is a summary carrying salient information on various aspects.

the ROUGE metric measures the number of matches between two pieces of text, so it is difficult to compare which one is more informative.


In addition to the ROUGE metric, we conducted the informative test for quality evaluation.

for a large corpus or multiple systems comparison, this test requires a huge amount of human effort.


(2018) might be the only exception to the above, showing that the effectiveness of incorporating discourse information into an RNN-based summarizer for scientific papers by treating the source document as a sequence of sections such as "Introduction" or "Conclusion".

they were not able to show how the tree-like discourse structure is effective in RNN-based approaches for extractive single-document summarization.


Neural network-based approaches achieved similar scores on the abstractive test set because the length constraint is long; specifically it was set to 100-words.

graph-based approaches (TGRAPH and URANK) performed better than the neural network-based approaches.


The choice of a bigram HMM for tagging is clearly suboptimal.

our objective here is an initial proof-of-concept demonstrating that tagging in low-resource settings can benefit from unsupervised morphology.


In order to approximate the outer expectation, we use Markov Chain Monte Carlo sampling over untagged graphs as described by (Sumalvico, 2017).

the sampling algorithm will need some modifications, as contrary to the original approach, the edge probabilities are not independent of the graph structure.


Weakly supervised Word Sense Disambiguation (WSD) methods express very good coverage that is only limited by the coverage of the underlying knowledge base used to define word senses and provide a basis for their disambiguation.

very good results are obtained only when a rich and large knowledge base is utilised.


There are no known systems that handle NPE detection and resolution in English.

on a related linguistic phenomenon called oneanaphora or one-substitution, in which the elided noun is replaced by an overt pro-form, there is a thorough data-driven investigation (Gardiner, 2003) and machine-learning methods that use heuristics proposed in this study (Ng et al., 2005).


There are no dedicated linguistic resources or datasets for the analysis of NPE in English.

there are many well-known corpora that contain annotated instances of NPE.


Maybe a case can be that these factors are the deep semantic features we have briefly mentioned in the introduction section.

a definitive answer to what these factors are and how to account for them goes beyond this research.


In this context, the results of monitoring feedback, reactions and emotions are of great value as they fuel decisions and behaviors (Tversky and Kahneman, 1989).

most of the existing solutions are still limited to manual annotations and simplified analysis methods.


That means, the training objective of the systems remains unchanged: they are required to correctly predict the value of the binary label at the first annotation layer.

when we analyze and evaluate the performance of the systems, we make use of both the binary and the atomic annotation layers.


Examples of pairs that were wrongly labeled as cognates by the classifier relying on orthographic information and that are correctly labeled as non-cognates by the combined classifier are: affects-effecten, unlocking-blokkering, investments-instrument, slit-gesplit, provide-profielen, brazier-branden, might-high, where-wateren.

figure 1: Conditional Inference Tree with depth 3 trained on the orthographic similarity features.


We train supervised neural network models for argument classification using Russian semantically annotated corpus-FrameBank.

we note that this resource provides annotations only to a very limited set of predicates.


These phrases that bear temporal, locative, and some other types of information are correctly identified by our parser but are considered as mistakes in the evaluation setup resulting in lower precision than it actually is.

we see that it is the only adequate way to assess extraction quality with partially labeled data.


This is because the subspace corresponding to dog is not a subspace of pet , in particular because dog is not orthogonal to the basis vector − −−−− → working.


WordNet contains verb hyponymy relationships, and therefore we can use similar methods to extract lists of hyponyms.

we cannot use Hearst patterns to collate verb hyponymy relationships.


For example, one of the hyponymy relationships not picked up in WordNet is (oven, device).

there are a number of other instances such as (electric appliance, device) that are similar enough to oven that device can be understood as includ-ing oven.


Crowdsourcing (Howe, 2006) is one promising approach that can be leveraged for the task of LR creation.

any crowdsourcing ambition is faced with the challenge of attracting and retaining crowdworkers and with safeguarding the quality of results produced by the crowd (Daniel et al., 2018).


In addition, it shows a substantial accuracy increase in turn compression and audit more OOP cases.

sMN surpasses MMN in reordering and another slot OOP challenges.


Especially, sentiment analysis has received a considerable attention from both industry and research community.

only a few research examples exist for Azerbaijani language.


Table 2 presents that combination of frequency based vectorizer and bigram model provided the highest F1 score result of 95.47 percent.

the lowest F1 score result we got among the above combinations was 90.9 percent and was obtained from tf-idf and trigram model.


These results may have been determined to a great extent by the unbalance in the parallel sentences.

we could see a slight improvement compared to the results using only the small parallel data in this architecture.


Table 7: Original (src), predicted (norm) and target (tgt) sentences using the NMT approach trained on the extended dataset.

the system still produces a large number of odd normalizations.


At the same time, companies need reviews on their products or services in order to get feedback and maintain good reputation.

not all reviews available in the Internet are genuine.


As the machine learning algorithm we used Random Forest (RF) given that it was reported as one of the most effective in FR detection (Chowdhary and Pandit, 2018;Saumya and Singh, 2018;Viviani and Pasi, 2017).

any other learning algorithm can be applied instead.


that dataset was originally used for multi-class classification.


Most answers (50.3%) state that replication was done on MSc or PhD level, less on PostDoc level (20.7%) and slightly more as Faculty members (24.3%).

we did not find strong correlations between the respondents' position and opinion on the importance (or lack thereof) of reproducibility.


It is difficult to compute an accurate response rate for the survey, because we do not know the extent of the population comprised by subscribers to the mailing lists and professional networks that we reached out to.

if we approximate the target population using the average participation in a *ACL confer- ence (N=1,000), we can estimate the response rate at about 20%.


We believe that, in the case of GloVe, where the word embeddings are context-independent, POS tags can improve token representations to also take into account the textual context in which the tokens have occurred.

since ELMo already takes into account the textual context when extracting the token representations, POS tags do not help much and can even be redundant in some cases.


We believe that this is due to the more deterministic nature of SVM in comparison to the neural networks.

the most important drawback of the SVM was the training time: since the SVM classifier was trained separately from feature extractors, training it entailed additional training time to the model.


The features from the third dialogue turn in models with GRU have slightly higher mutual information than the ones from the models with LSTM.

the standard deviation between the features extracted by the GRUbased models are significantly smaller than the ones with LSTM, showing that between the models with LSTM and the ones with GRU, the features extracted from the models with GRU had more similar amount of contribution to the classification task than the ones from the LSTM.


Word meaning change can be inferred from drifts of time-varying word embeddings.

temporal data may be too sparse to build robust word embeddings and to discriminate significant drifts from noise.


It can involve morphological information (Luong et al., 2013) derived from the character level (Santos and Zadrozny, 2014; Labeau et al., 2015), and often make use of external resources: semantic lexicon (Faruqui et al., 2015), and pre-trained embeddings from larger corpora (Komiya and Shinnou, 2018).

to our knowledge, no work has attempted to apply similar solutions to the problem of sparse data in temporal corpora, even thought this situation has been faced by many authors, often in the case of short time steps for social media data (Stewart et al., 2017;Bamler and Mandt, 2017;Kulkarni et al., 2015).


The DBE model has a regularisation term (equation 6) to enforce this property, and a majority of words have a very low drift on the histogram.

on 1 % of the dataset, this model can not discriminate very high drifts from the rest.


To conclude, both the DBE and DSG model are able to detect directed drifts even in the 1 % subset of the NYT corpus, while the ISG can not.

the drift distributions of the DBE and DSG models have a much shorter shorter tail on the 1 % subset than on the 10 % subset: they are not able to discriminate very high drifts from the rest of the words in extreme scarcity situation.


This property, as long as the ability to detect directed drift, are two important properties of a diachronic model.

both have low ability to discriminate the highest drifts on a very small dataset.


Polysemy is a crucial topic when dealing with diachronic word embeddings, as the change in usage of a word can reflect various changes in its meaning.

the more different senses are taken into account, the more data is needed to tackle it.


Maximum matching is a fast deterministic method for morphological analysis.

the method tends to decrease performance due to lack of consideration of contextual information.


Previous works such as SPARQL2NL  and SPARTIQULATION (Ell et al., 2012) have already shown the usefulness of the verbalization of SPARQL 1 and RDF in areas such as question answering (Lehmann et al., 2012) and the explanation of the output of systems based onSW technologies .

other SW languages are rarely investigated, such as OWL.


According to Gatt and Krahmer (2017), there has been a plenty of works which investigated the generation of NL texts from Semantic Web Technologies (SWT) as an input data (Cimiano et al., 2013;Duma and Klein, 2013;Ell and Harth, 2014;Biran and McKeown, 2015).

the subject of research has only recently gained significant momentum due to the great number of published works in the WebNLG (Colin et al., 2016) challenge along with deep learning techniques (Sleimi and .


As expected, the verbalization of short expressions leads to sentences which read as if they have been generated by a human.

due to the complexity of the semantics that can be expressed by the languages at hand, long expressions can sound mildly artificial.


There is no established medical or biological ontology for Romanian language.

public information is readily available in both structured and unstructured format.


There are no datasets containing information on what drugs should be administered for certain symptom in Romanian language.

a usage section exists in each medical leaflet describing cases in which the drug should be taken (e.g., to numb pain, to reduce coughing, to lower body temperature and eliminate fever, etc.).


In most simple use cases, the first entries are relevant.

the symptom of a disease can be, in some cases, the effect of a drug that is targeted against a totally different disease.


A quick look at an image is sufficient for a human to say a few words related to that image.

this very easy task for humans is a very difficult task for the existing computer vision systems.


Current dominant computational methods and tools are mostly used on languages with large corpora, following a statistical approach to train their systems according to a relevant task.

with little data at hand these methods may not present a good solution.


This is an important strategy to form a 'common source lexicon' (Beesley and Karttunen, 2003).

for the sake of simplicity, we have excluded nominal and verbal compounds from our lexicon.


The detection of quotations (i.e., reported speech, thought, and writing) has established itself as an NLP analysis task.

state-of-the-art models have been developed on the basis of specific corpora and incorporate a high degree of corpus-specific assumptions and knowledge, which leads to fragmentation.


In this sense, SNNs are similar to pre-trained word embeddings, since a SNN trained for a commonly used Knowledge Base (i.e., DBPedia or Word-Net) could be useful in solving different problems.

pre-trained SNNs can (and should) be fine-tuned on a specific problem L after deciding a convenient deep learning architecture for this problem.


automatic post-editing (APE) (Junczys-Dowmunt and Grundkiewicz, 2016;.

the effects of various parameters for creating back-translated (BT) data have not been investigated enough as to indicate what are the optimal conditions in not only creating but also employing such data to train high-quality neural machine translation (NMT) systems.


These data can even reach better performance than adding the same amount of authentic data, mostly by reducing the number of mistranslations, and increasing the lexical and syntactic variety in a positive way (introducing useful new instances).

if the amount of synthetic data becomes too large (three times larger than the authentic baseline data), the benefits of hybrid system start to diminish.


To extract n-grams we use TopMine algorithm (El-Kishky et al., 2014) based on a words co-occurrences statistics.

we found it beneficial to implement some modifications.


These algorithms traverse over the relations and nodes in a large knowledge base such as WordNet (Miller, 1995) or taxonomies built from Wikipedia links (Cucerzan, 2007) to uncover relationships between the nodes in the knowledge base.

the effectiveness of shallow neural networks in learning word similarity, as shown by the popular Word2Vec and GloVe models (Mikolov et al., 2013;Pennington et al., 2014), rapidly replaced traditional graph-based methods for word similarity and prediction tasks.


For the unique identifier, the frame name is prepended to the name of the FE together with a colon separator, such as HIRING::EMPLOYEE.

these unique FE nodes are further con-nected to their abstract counterparts with an is-a relation (REVENGE::AVENGER is-a AVENGER), which preserves the connectedness of the FE in FrameNet and also provides some connectivity between frames that share the same FE.


Their predicates are mostly restricted to one sense -ORIGIN, for instance, has predicates jamaican.n, canadian.n, and french.a, while NATURAL FEATURES has predicates mountain.n and continent.n.

their frame elements are more likely to appear across different domains.


Therefore, ambiguity of nouns, verbs and pronouns has been investigated extensively in recent years (Guillou et al., 2018;Müller et al., 2018;Rios Gonzales et al., 2017.

to the best of our knowledge, no results for ambiguity of conjunctions have been reported so far.


The rest 8% drop can be attributed to the model's inability to learn good embeddings for unique NEs that were rarely seen during training.

these issues do not pose a problem for our With-NE- Table model, since we generate embedding for a NE on the fly for each question based on the context.


The accuracy in performing the dialog (by selecting responses from candidate set) is similar for both the models on the normal test set.

in the OOV-test set, for task 1, where the system has to maintain the dialog state to track which attribute values have not been provided by the user yet, W/O-NE- Table model  Detailed results for task 4 are shown in Table 5.


The candidate response retrieval (dialog) is same as the above model and the column attentions are also similar.

the models differ with respect to attention over rows.


But contextualised word embeddings showed a great   As shown in the above section Smooth Inverse Frequency with ELMo and BERT stacked contex-tualised word representations provided the best result.

since we used the cosine similarity between two vectors, the predictions of our model are constrained to follow the cosine curve and are thus not suited for these evaluation metrics.


The Siamese neural network with GRU was tested with a cyclical learning rate (Smith, 2015), which has the advantage of forcing the model to find another local minimum if the current minimum is not robust and makes the model generalize better to unseen data.

neither cyclical learning rate nor reducing learning rate on plateau increased the per- 3 These results are reported in Marelli et al.


Mueller and Thyagarajan (2016) report that their architecture does not perform well with active-passive equivalence.

as shown in   our architecture performs slightly better than the LSTM based architecture.


First, cts are extracted based on their POS pattern.

rather than a predefined list, the patterns are obtained from the annotated training corpus.


It is less reliant on frequency, leading to fewer mistakes on rare terms or frequent non-terms.

the supervised system also has a distinct weakness, namely its domain-dependence, since it was trained on outof-domain data.


On the one hand, there has been a growing interest in the linguistic information that may be ultimately encoded in vectorial representations (Be-linkov et al., 2017;Conneau et al., 2018), also relating to their eventual "universality", in view of possibly transferring these representations from one language processing task or application to another (Shi et al., 2016;Cífka and Bojar, 2018).

growing attention has been devoted to sources of information for word embeddings other than what may be conveyed and extracted from co-occurrences in text.


Recurrent Neural Network LMs composed of LSTM units, especially those augmented with an external memory, have achieved state-of-theart results.

these models still struggle to process long sequences which are more likely to contain long-distance dependencies because of information fading and a bias towards more recent information.


Recently, Recurrent Neural Networks LMs (RNN-LMs) have became the state-of-the-art approach to LMs (Józefowicz et al., 2016).

rNN-LMs struggle to keep their level of performance as the length of the input increases.


It might appear that this architecture was using a strategy of "pay equal attention to everything in the past".

we argue this interpretation ignores the power of the LSTM gating mechanism.


In fact, Ostmeyer and Cowell 2017have presented a model that computes a recurrent weighted average (RWA) over every past hidden state.

the authors limit themselves to evaluate the model over simple tasks and the effectiveness of that model over language modelling is still to be demonstrated.


(2017) on this dataset, the results are encouraging given the simplicity of the Averaging RNN-LM.

we should note that none of these models perform at the same level of the state-ofthe-art models such as those of  and Takase et al.


Furthermore, implementing this strategy using an average of past states is also a relatively blunt way of instantiating this approach.

as our results demonstrate this simple approach is effective and we understand this is a starting point.


Their meaning is assigned to the whole form, they cannot be divided into smaller units if we want to keep the original meaning.


On the one hand, we see that 70-80 percent of the pVCCs are completely correct, which can be considered a high value in itself.

only one single real error is found among 40 constructions which is only 2.5 percent.


After we processed this corpus using our offensive language detection system, the comments in this corpus include annotations expressing their offensive probabilities.

as we aim to analyse conversations as turn-based dialogues around offensive comments, we now have to filter the corpus and extract such linear dialogues.


While the vast majority of submissions (approximately 74%) have very few replies (≤ 10 comments), only 80 submission have more than 1000 comments.

there is a substantial amount of threads with more than 10 comments and the average number of comments per thread -if we exclude threads with none or only one comment -is 54.


Morphological Vectors The nominal system of Bulgarian shares some features with other Slavic languages, such as agreement in grammatical gender and number, rich pronoun system, etc.

it has also specific features, such as the postpositioned definite article and lost nominal declension system.


We then concatenated the vector to the rest of the word embeddings.

this approach did not result in significant improvements because new words are added to the language almost every day, and it is impossible to capture them all.


The information extraction task in cybersecurity domain has been discussed in several works.

the vast majority of the works consider information extraction only from structured or semi-structured English texts.


A straightforward example is the Italian-Spanish pair (notte, noche), with a similar form and common meaning.

many cognates have similar surface forms, but differ in meaning, such as the English-French (actual, actuel) and Finnish-Estonian (huvittava, huvitav).


In other words, e 2 is "twice removed" from e 1 , being translated once by a human and once automatically.

f 1 = HT (e 1 ) and f 2 = M T (e 1 ); both f 1 and f 2 are only "once removed" from e 1 : f 1 was translated manually and f 2 automatically, but only once.


For both ATS systems used, it was found that (with this minimal human correction of the simplified output) such a pre-processing step improves fluency of the translations, and reduce the post-editing effort.

the authors only considered manually post-edited ATS output and made no experiments with the raw (uncorrected) ATS output.


Given that here we just look for the proof of concept, we wanted to ensure that we correctly assign G and M scores, and this was thus done manually.

it is important to note that several systems for automatic assignment of G and M scores to the ATS outputs have been proposed up to date (Štajner et al., 2016).


This is not unexpected, since the English data set is larger than the German one.

the best English results are reached by XG-Boost while the same classifier only reaches 59.50 F for German.


While the data sets were collected independently and annotated with different classes, collapsing them into a binary annotation between abusive and nonabusive language resulted in data sets that superficially showed similar characteristics.

our investigation has shown notable differences: For English, XGBoost provides the best performance, for German SVMs do.


We also hypothesize that the attention distribution in Trans-noEnc is not spread out enough for extract-ing contextual features.

we find that word embeddings and attention distributions are not the major reasons causing the distinct gap.


One potential explanation is a suboptimal setting of the α parameter (Equation (3)) that would let the joint model pay too much attention to the numeric attributes.

figure 3 indicates that this not the case: the change in performance on categorical attributes is relatively minor across values of α, in particular compared to the chance in performance on numeric attributes.


Finally, on a methodological front, a common practice to measure the semantic shift of words between consecutive time periods is to calculate their displacement error that results from "aligning" word vector representations across these time periods (Hamilton et al., 2016).

a subset of these words may have actually undergone semantic change and thus trying to align their representations across time is counter-intuitive for the task of semantic change detection, and -importantly -can result in drop in performance.


Another strand of work focuses on generating diachronic word embeddings (Kutuzov et al., 2018), aiming to learn word representations across time (Bamler and Mandt, 2017;Rosenfeld and Erk, 2018;Yao et al., 2018;Rudolph and Blei, 2018).

these are often hard and slow to train under a massive dataset, such as the UK Web Archive.


Both figures demonstrate that the semantic change of our ground-truth is captured through our representations.

it is also demonstrated that semantic shift is a gradual process and its level may vary across different words.


The model translates the Turkish word santralin (of the power plant) as voimalaa (in the power plant) instead of voimalan.

the majority of errors have incorrectly translated root words with correct inflections.


Table 5: Sentences with prediction type (for the multi model, with respect to the target medium), the target medium, and total number of media that selected this sentence (#).

rows 7 and 8 show claims that are not check-worthy for any source, but exhibit features such as named entities and negations that typically suggest that the claim might be check-worthy.


Text summarization is not considered to be primarily a simplification task.

it becomes useful when long documents are involved as it aims to reduce text processing time and thus to access quicker the main concepts of the document.


(2019) further explore this idea, showing that text summarization does not maintain the same degree of text complexity as the original document.

based on their experiments with a corpus of unsimplified newswire documents for native speakers DUC 2002 1 and contrary to the observation of Petersen and Ostendorf (2007), they conclude that on some readability metrics the summaries generated with the compression rate of 20% score better and are easier to comprehend than the original texts.


and treat summarization as an optimization problem to maximize the average informative score of the summary and to improve its readability.

their set of readability features is small and they do not study the relative importance of each feature with respect to the corpus or the target language proficiency level.


As mentioned in Section 2 the OneStopEnglish corpus (Vajjala and Lučić, 2018) is a similar resource for readability assessment that contains the same texts rewritten for elementary, intermediate or advanced reading proficiency levels.

given the 7 levels from the BNE dataset we expect to be able to identify the more subtle readability differences between them.


In the case of TextRank, the similarity between two sentences is calculated as the number of words they have in common normalized by sentence length.

to LexRank, TextRank recursively changes the weights of the sentences incorporating in this manner how all the sentences in the graph relate to each other.


Most of the available datasets for deep learning belong to the domain of news text that further restricts the application of these methods to other domains.

semantic-based approaches rely on a parser to transform text to its semantic representation and, therefore, a poor parser performance will reduce the quality of generated summaries.


The summarization method based on BSUs proposed by Li (2015) overcomes the limitation of text generation faced by AMR graphs, and produces informative, coherent and compact summaries.

as the authors state, the BSU network cannot yet handle data that express opinions rather than facts and actions, since these cases involve verbs that lack meaningful actions, such as 'be', and the underlying representation of actoraction-receiver cannot be appropriately computed.


Contrary to Genest and Lapalme (2011) we do not incorporate any manual rules to reject candidate InIts.

a small portion of them will be lost during the surface realization stage if SimpleNLG fails to generate a sentence from an InIt.


Given the fact that there are many different types of word embedding models, we argue that having a systematic pipeline to evaluate, extract, and visualize word embeddings for a downstream NLP task, is important but non-trivial.

to our knowledge, there is no single comprehensive pipeline (or toolkit) which can perform all the tasks of evaluation, extraction, and visualization.


(2013) used accuracy metric to measure the quality of word embeddings on the task in which only when the expected word is on top of the prediction list, then the model gets +1 for true positive count.

this is not a well-suited metric in low resource languages where training corpus is relatively small, i.e., 233M tokens in Vietnamese Wiki compared to 6B tokens in Google News corpus.


The differences between the two models are small (about 0.02), which leads to the conclusion that both methods are worth considering in further research.

a comparative study in (Östling, 2018) shows that better results are achieved with good manual processing of features than with extending and deepening the architecture.


What is more, for Euclidean distance, data scaling may blur the distances between points and worsen the quality of the solution.

usage of standardisation methods with Agglomerative Clustering (AC) algorithm improves obtained results.


The majority of machine learning research assumes that the data used for building a model and making inference are sampled from the same distribution, i.e., the data generation process is stationary.

because of its online characteristic, one challenge faced by FSD models is that the system's vocabulary (and hence document representation) cannot be derived from a target corpus, but must instead be defined by the vocabulary of a background corpus.


It should be noted that we can look at all the terms of the target corpus here because we are now doing the analysis.

during the real FSD we will never know whether a term from the background corpus appears in the target corpus or not.


If there are no terms with the same document frequency in an ordered term list, the index of each term can be reasonably considered as its rank in this corpus.

the fact is that many terms have the same document frequency in a corpus, so they should have the same rank.


We find that all corpora that are more similar (in terms of term distributions) to the target corpus lead to better performance in FSD, except in the case of very similar performance between COCA and COCA + COHA.

it is worth noting that only six in nine corpora that have a larger scale of common terms correspond with better FSD performance while the other three do not.


First, participants with and without autism alike had a similar disposition towards reading product reviews and the amount of reviews they read before coming to a decision (less than 10).

the group with autism consistently perceived online product reviews as being more difficult to understand compared to the control group (Q3).


* Equal contribution Traditional machine learning algorithms such as K-Nearest Neighbor, Support Vector Machines (SVM), and Naive Bayes have been widely used for text classification tasks and accuracy levels of over 80% have been reported in the literature for various data sets (Kadhim, 2019).

obtaining a similar level of success for short-text classification is difficult, since short-texts contain smaller number of words compared to lengthy texts, which makes classifying them effectively a challenging task (Taksa et al., 2007).


Before the work presented in this paper, no full parser existed for Icelandic.

a shallow parser, IceParser, has been developed for the language (Loftsson and Rögnvaldsson, 2007).


not requiring any ex ante transformation of the CFG).

performance has been seen as a problem when parsing heavily ambiguous sentences, with both time and space requirements being worstcase cubic (O(N 3 )) in the sentence length N. Tomita (1986) described Shared Packed Parse Forests (SPPFs), a data structure that avoids redundancy when generating and storing parse trees for ambiguous token spans.


see (Loveland, 1978) and (Fitting, 1996)).

the sophistication of such formal semantic models comes at a cost: the complex set of rules allowing for the logical interpretation of text must either be provided a priori, or learned.


Finally,Markov Logic Networks (Richardson and Domingos, 2006) (MLNs) are used to perform weighted inference on the resulting knowledge base.

they only employed single-word distributional similarity rules, and only evaluated on a small set of short, hand-crafted test sentences.


The different results yielded from TakeLab depending on the training data suggests that some STS systems are quite sensitive to the source of the sentence pairs, indicating that domain adaptation techniques could have a role in this task.

dKPro performed extremely well when trained on all available training, with no special tweaking for each dataset.


It provides a more precise context but only works for comparing words within the same POS.

a context window of ±4 words allows us to compute semantic similarity between words with different POS.


First we hypothesize that STS similarity between two sentences can be computed using where T is the term alignments score, P ′ is the penalty for bad term alignments and P ′′ is the penalty for syntactic contradictions led by the alignments.

p ′′ had not been fully implemented and was not used in our STS submissions.


For example, the sentences in the Figure 1 both illustrate "somebody is moving".

our model depends on the precision of that parser.


Generally, all results outperform the baseline, based on simple word overlap.

the performance of Model_LIM is not always the best in the three runs for each dataset.


Performance is particularly high when capitalisation is included (VSM w caps).

this approach still outperforms the baseline without case information (VSM w/o caps), demonstrating that using this simple approach is less effective than making use of local context.


Most automatic speech recognition (ASR) systems do not provide capitalization.

our system does not rely on capitalization information, and therefore can identify proper / common nouns even if capitalization is absent.


For the 2012 STS task, Heilman and Madnani (2012) submitted the PERP system, which performed competitively in relation to other submissions.

approaches including word and n-gram features also performed well (Bär et al., 2012; Sarić et al., 2012), and the 2013 STS task focused more on predicting similarity for text pairs from new domains.


This process has to be repeated for nouns (see Table 4), verbs, adjective, adverbs, prepositions, conjunctions, pronouns, determinants, modifiers, digits and date times.

the tables have to be created only with the similar groups of the sentences.


Conversely, UNIBA-2STEPSML reported a different trend showing its weakness with respect to a high variance in the data, and performing worse than UNIBA-DSM PERM on the OnWN and SMTnews datasets.

the evaluation results have refuted our hypothesis, even with the use of the stacking system.


Examples include automatic text summarization, question answering, essay grading, among others.

despite having important applications, semantic similarity identification at the level of short text fragments is a relatively recent area of investigation.


The generated phrase table contains candidate phrase to phrase translation pairs with source-to-target and target-to source translation probabilities.

the open source Moses SMT system (Koehn et al., 2007) For each English Phrase "e1" { @ar_phrases = list of Arabic Phrases aligned to "e" in the phrase table; For each a (@ar_phrases) { @en_phrases = list of English phrases aligned to "a" in the phrase table; For each e2 (@en_phrases) { $Prob(e2\e1) = Prob(a\e1)*Prob(e2\a); } } } can be used in the same way to generate a synonyms dictionary from phrase table.


Run6 as illustrated before was planned to measure idfweighted lexical matching feature only, over Porter stemmed, and stemmed synonyms words.

when revising this experiment set up during preparing the paper, after the evaluation period, we found that the English-to-English synonyms table was not correctly loaded during matching, thus skipping synonyms matching feature from this run.


Our motivation is to use this model by adjusting the parameters α and β for better modeling human similarity judgments for short texts.

this is not a symmetric model and the parameters α and β, have the dual interpretation of modeling the asymmetry in the referent selection, while controlling the balance between |A ∩ B| and |A − B| + |B − A| as well.


The locality of the dependency representation causes this set of three-arcs structures to be large, sparse and noisy -many of the relations may appear random because some arcs are in many cases almost independent given the others.

some of the relations are known to be of interest, and we hope more of them will prove to be of interest in the future.


Traditionally, a number of criteria (such as compounds being syntactically inseparable, and that compounds have a specific stress pattern) have been proposed, in order to establish a border between compounds and non-compounds.

lieber and Stekauer (2009a) demonstrated that none of these tests are universally reliable to distinguish compounds from other types of derived words.


3 They computed backward and forward entropy to try to remedy the problem with especially high-frequency phrases.

their assumptions were not easily generalisable across languages, e.g., they assume that the relative frequency of a specific type of MWE (light verb constructions) in Persian is much greater than in English.


Compared to more traditional approaches based on hand-coded knowledge (Fass, 1991;Martin, 1990;Narayanan, 1997;Narayanan, 1999;Feldman and Narayanan, 2004; Barnden and Lee, 2002;Agerri et al., 2007), these more recent methods tend to have a wider coverage, as well as be more efficient, accurate and robust.

even the statistical metaphor processing approaches so far often focused on a limited domain or a subset of phenomena (Gedigian et al., 2006;Krishnakumaran and Zhu, 2007), and required training data Turney et al., 2011), often resulting in a limited coverage.


Metaphor identification systems annotate metaphorical language in text, and metaphor interpretation systems discover literal meanings of the previously annotated expressions.

cognitive evidence suggests that humans are likely to perform identification and interpretation simultaneously, as part of a holistic metaphor comprehension process (Coulson, 2008;Utsumi, 2011;Gibbs and Colston, 2012).


For example, a representative survey asking 1,000 people by a leading polling company costs to the order of $10,000 1 .

once the software exists, social media data from tens of millions of users can be obtained and analyzed at a fraction of the cost.


We conjecture that their low gain in precision could be a side effect triggered by two factors: on the one hand, their model crucially relies on coreference chains, which are automatically generated for the test set and hence are rather noisy.

their heuristically created training data might not represent implicit argument instances adequately.


If both documents achieve the same score, we neither count the test item as correctly nor as incorrectly classified.

we apply our own model only on the document that contains the implicit argument, and use the classifier to predict whether this realization type fits into the given context or not.


For instance, "'Being Spiderman is a dream come true,' says Andrew Garfield" is a short sentence mentioning two entities that participate in the acted in relation.

none of the words in the sentence are particularly indicative of acted in, and if FreeParser were to use this as a training sentence, it would most likely learn a wrong lexical entry.


Furthermore, most, if not all, of previous work on SST has relied on gold standard part-of-speech (POS) tags as input.

in a domain such as Twitter, which has proven to be challenging for POS tagging (Foster et al., 2011;Ritter et al., 2011), results obtained under the assumption of available perfect POS information are almost meaningless for any real-life application.


We present an approach to weakly supervised SST based on type-constrained EM-trained second-order HMMs (HMM2s) with continuous word representations.

when using supervised learning, we can distinguish between two degrees of supervision for domain adaptation.


Research in emotion analysis of text suggest that emotion lexicon based features are superior to corpus based n-gram features.

the static nature of the general purpose emotion lexicons make them less suited to social media analysis, where the need to adopt to changes in vocabulary usage and context is crucial.


The formulation in the previous section generates a word-emotion matrix L by observing the term frequencies within a class.

term frequencies alone do not capture the term-class associations, because not all frequently occurring terms exhibit the characteristics of a class.


The results suggest that the PMI-lex was able to leverage the availability of adequate training examples to learn the patterns about emotions such as anger, joy, sadness and love.

given that not all emotions are widely expressed a lexicon generation method that relies heavily on abundant training data could be ineffective to mine less represented emotions.


We expected that modelling a document to exhibit more than one emotion (EM-allclasslex) would better distinguish the class boundaries.

given that tweets are very short it was observed that modelling a document as a mixture of emotion terms and general terms (EM-classcorpus-lex) yielded better results.


In working with deep representations such as MRSes or dependency parses, there are myriad sub-graphs that can be used as features.

these features are often quite sparse and do not generalize well.


Before modifying the baseline system's code, we reproduced their reported accuracy figure of 88.80% over the entire 25,000 review test set.

it appears the baseline system used the test data as development data.


Such a corpus could be obtained by performing word sense disambiguating (Agirre and Edmonds, 2006;Navigli, 2009) on all words.

as this is an error-prone task and we are more interested to showcase the overall principle, we rely on Wikipedia as an already manually disambiguated corpus.


The authors do not report any overall tendency of results.

these experiments show that switching from words to senses has an effect on the performance of similarity measures.


Mihalcea (2005) or Sinha and Mihalcea (2007)] (Note that this subgraph is a hybrid, because only its vertices belong to G) In practice, subgraph edges may be directed, weighted, collapsed, or filtered.

to keep the distinctions between subgraph types simple, we do not include this in our formalisation.


Therefore in our results the first sense is chosen which is incorrect.

if  The shortest paths cup#1→handle#1→golf_club#2 and cup#7→golf#1→golf_club#2 only exist because the sense golf_club#2 (anchored to the more polysemous lemma club) is present, if it was not then the SENSE_SHIFTS filter would have removed these alternative senses.


It also assumes that the articles are topically orthogonal.

recent work (Gottron et al., 2011) has shown that by using the documents from Reuters corpus instead of Wikipedia articles can also achieve comparable results.


Figure 2 shows that the proposed methods of context enrichment significantly improve over the ESA scores for different values of N.  includes the context about the word "car" at surface level rather than at the semantic level.

eSA CeWikiDef only includes the definition if it does not refer to more than one semantic sense, therefore, eSA CeWikiDef outperforms all other types of context enrichment.


Experiments would need to be carried out on common word space evaluation tasks in order to compare its performance to that of HAL and other word space models.

our results suggest that the tensorbased methods are more robust than the basic HAL model to a certain extent, and can improve accuracy.


We can train a standard linear classifier on the generated training instances to learn the weight vector w. In the case of the IWIYW data, there is no explicit ranking between actions.

we are able to create ranking pairs for the IWIYW data in the following way.


The focus of the analysis described so far was on ways to improve recall in an email categorization system: We count the inference steps required to increase the amount of mappable information (similar to query expansion in information retrieval).

the figures do not show the impact of these mappings on precision, i.e., whether an inference step we take would negatively affect the precision of the system.


Sentence Connectivity is a textual characteristic that may be incorporated intelligently for the selection of sentences of a well meaning summary.

the existing summarization methods do not utilize its potential fully.


Hence, the current results could be interpreted as moderately substantial agreement.

artstein and Poesio 2008, p. 591, question this received wisdom and state that "only values above 0.8 ensured an annotation of reasonable quality".


Furthermore, some emphasis is put on volitionality in Sowa's system: the initiatior of an action can be either Agent or Effector, depending on whether (s)he causes the action voluntarily or not -something that is often difficult to decide even when a context of a sentence is given.

the Agent role is extended in VerbNet to 'internally controlled subjects such as forces and machines', but it is easy to confuse this role with Theme.


Given a simple transition, a process can be viewed as simply an iteration of ν (Fernando, 2009).

as (Pustejovsky, 2013a) points out, since most manner motion verbs in language are actually directed processes, simple decompositions into change-of-location are inadequate.


The dependency trees define the path that was taken through the generative process in Figure 3 and are used to create a Bayesian network for every sentence, like in Figure 2.

object alignments a i are latent during learning and we must marginalize over them.


The coref model outperformed the simple windowing method under every tested configuration.

the improvement over windowing with n = 1 is not significant.


The creation of e-learning courses is generally a time consuming effort.

separating text into topically cohesive segments can help to reduce this effort whenever textual content is already available but not properly structured according to e-learning standards.


In a sentence processing model, these nonlocal continuation dependencies would be incrementally calculated in working memory in a manner similar to coreference resolution.

5 in this paper, in order to obtain a reasonable estimate of the learnability of such a system, continuation dependencies are assigned post-hoc by a statistical inference algorithm.


They partition the comparison class into three disjoint sets: a positive extension, a negative extension, and an extension gap (entities for which the predicate is neither true nor false).

degree-based approaches assume a measure function m mapping individuals x to degrees on a particular scale (degrees of height, degrees of darkness, etc.)


Indeed, most linguistic approaches to vagueness focus on uni-dimensional predicates such as 'tall'.

many vague predicates are multi-dimensional, including nouns for positions ('above'), shapes ('hexagonal'), and colours ('green'), amongst many others.


It is customary to represent the output of these operations directly in a dense lowdimensional space.

the asymmetric similarity measures we use assume sparse vectors (or the "inclusion" criterion would be meaningless), so we project back the outcome of SVD and NMF to sparse 10K-dimensional but low-rank spaces.


A good model must be able to capture both factors (and this is what we tested above).

we are also interested in assessing to what extent the models are capturing the modification effectproper, as opposed to the overall degree of typicality of the h concept as member of the c category.


Some of these assumptions have led other researchers to argue that prototypes do not compose at all (Connolly et al., 2007).

the approach we borrow from distributional semantics, while only mildly successful for now, has the advantage of being very simple both in its con-struction and application, and in the assumptions that it makes.


Particle verbs may share the semantics of the base verb and the particle to a variable degree.

while syntactic subcategorization frames tend to be good predictor for the semantics of verbs in general (verbs that are similar in meaning also tend to have similar subcategorization frames and selectional preferences), there are regular changes in subcategorization frames by particle verbs with regard to the corresponding base verbs.


For example, these resources would include multiple meanings for the word ball including the 'event' and 'sports equipment' senses.

the fact that there are multiple meanings associated with ambiguous lexical items can also be problematic since it may not be straightforward to identify which one is being used for an instance of an ambiguous word in text.


the fact that the support to evaluate the plausibility of a consequence ψ ∈ Ψ K i was limited.

performances were generally poor when the cardinality of Ψ rand K i was important (> 0.25 * |Ψ K i |), which may be explained by the fact that support sets for some classes of F were significantly modified after the extension of F with φ i .


In Experiments 1 and 2, we have found an improvement for including out-of-domain data.

it is unclear so far whether the improvements are simply due to the increased amount of training data, or to the training data becoming more varied.


John stuffed the letter in the envelope.

not all syntactic structures of the form (NP-SBJ (V NP PP)): 6.


The previous experiment on removing NON-CMC verbs effectively brought up the percentage of positive labels for the CMC and DISPLACE labels to 20% and 11%, respectively.

label proportions of 20-80 or, worse, 11-89 are still highly unbalanced.


We take this as an indication of the good quality of our clusters.

the MFS on our clusters is considerably higher, which could mean that our algorithm has a tendency to lump together frequent senses, casting doubts on the quality of the clusters.


For example, WordNet senses #3 and #4 are grouped under the stative supersense, although the definition and use of the two senses are completely different.

our cluster #1 comprises the most frequent 4 senses.


Thus they deploy annotation schemes which assume downstream consumers of some granular negation representation, learnable from the annotated resource(s).

the language in both of them differs greatly from the language encountered in clinical text, making them unsuitable for our QA system requirements.


From a performance-driven point of view alone, negation detection systems trained over BioScope annotations are optimized to match the annotated spans in the corpus (as discussed in Section 2).

for a negation detection system followed by downstream components implementing negation-driven inference, spans alone are not sufficient-especially spans which do not align with syntax.


Clearly, there may be arbitrary intervening text in such, and similar, constructions, again making the learning task difficult.

e3: the mechanism by which IFNs mediate this inhibition has not been defined.


PAS-based view into sentences offers unambiguously uniform treatment of some of the issues highlighted in the previous section.

for example, going back to e2, and the rationale for including or excluding subjects in the scope of a negation, we observe that verb nodes in the PAS always have fully instantiated frames, with subject arguments bound to the figure 2: PAS for e3: "the mechanism by which IfNs mediate this inhibition has not been defined."


Our transformation obtained high accuracy (94.9%) for the clinical records.

it was a harder task for abstracts (66.1%) and papers (73.1%) which contain more complex sentences.


ESA inherently assumes that Wikipedia concepts are orthogonal to each other, therefore, it considers that two words are related only if they co-occur in the same articles.

two words can be related to each other even if they appear separately in related articles rather than cooccurring in the same articles.


The semantics of a word can be obtained from existing lexical resources like WordNet and FrameNet.

such lexical resources require domain expertise for defining the hierarchical structure, which makes their creation very expensive.


LSA performs singular value decomposition (SVD) to obtain a latent concept space.

eSA directly uses supervised topics such as Wikipedia concepts that are built manually, and considers that every concept is orthogonal to each other.


Therefore, it can be used for computing concept relatedness matrix C n,n , as required by the NESA model.

to text relatedness measures based on DSMs such as ESA, which do not distinguish between entity and text, DiSER differentiate between entity and its surface forms by using unique hyperlinks referring to entities in Wikipedia for encoding entities while building DSMs.


In order to obtain the hyperlinks for VSM-Hyperlink and DiSER, we retain only those text segments which have manually defined links provided by Wikipedia volunteers.

the volunteers may not create the link for every surface form appearing in the article content.


For instance, WordNet-based measures (H&S (Hirst and St-Onge, 1998), L&C (Leacock and Chodorow, 1998), Lesk (Banerjee and Pedersen, 2002), W&P (Wu and Palmer, 1994), Resnik (Resnik, 1995) J&C (Jiang and Conrath, 1997), Lin (Lin, 1998)) and Roget thesaurus-based measure (Jarmasz and Szpakowicz, 2004) achieved higher accuracy on MC30 and RG65 datasets.

these approaches may not fit well for the datasets that contain non-dictionary words, therefore, the accuracy of knowledge-based measures decrease significantly on other datasets.


Therefore, these measures obtained poor results on WN353Rel in contrast to WN353Sim dataset.

corpus-based measures performed well for both type of relations i.e.


For example, in the Semeval test dataset of 100 full-length academic papers, to be described later in the evaluation section, the n-grams control has, rule satisfies and become known all have a document frequency of 1.

phrases chosen by humans as keyphrases such as Query expansion which occurs in 9 documents and as a keyphrase in 4 or language models which occurs in 12 document and again in 4 of them as key, have relatively high document frequency counts.


SDRT, however, postulates graphs as the basic structure.

mapping to a lexically-grounded approach, like that in the PDTB, is certainly possible.


we obtain improvements when we modify the test phase, when we either leave out the verb or both the verb and the adjective in the composition.


In this way, activation frequencies of features become meaningful.

the model is still biased towards events with many hidden units.


The number of hidden units does not necessarily correlate with occurrence probabilities of events.

the idea of known triggers being prototypes for events is appealing, therefore we did not change this part of the model.


The more theory-oriented systems, such as Conceptual Dependency and NSM assume only a few dozen primitives, but have a disquieting tendency to add new elements as time goes by (Andrews, 2015).

the systems intended for teaching and communication, such as Basic English (Ogden, 1944) start with at least a thousand primitives, and assume that these need to be further supplemented by technical terms from various domains.


For overcoming these issues, relational similarity was combined with lexical similarity calculated based on the distributional information of words (Cederberg and Widdows, 2003;Snow et al., 2004;Turney, 2006a;Séaghdha and Copestake, 2009;Herdadelen and Baroni, 2009).

(Turney, 2006b;Turney, 2008a) showed that relational similarity cannot be improved using the distributional similarity of words.


However, (Turney, 2006b;Turney, 2008a) showed that relational similarity cannot be improved using the distributional similarity of words.

with the previous approaches that took into account lexical and relational information as a linear combination of lexical and relational similarity scores, the present work focuses on introducing word pair representations that merge and jointly represent types of information: lexical and relational.


Rizzo and Troncy (2012) describe the NERD system, which combines entity linkers.

we are not aware of a system that, like ours, makes an automatic choice among the systems' conflicting annotations, based on an estimate of each annotation's quality.


To the best of our knowledge, this is the first work to address the problem of Implicit Entity Recognition (IER) in clinical documents.

there is a large body of research that is relevant to the problem, including Named Entity Recognition (NER), Entity Linking (EL), Coreference Resolution, Paraphrase Recognition, and Textual Entailment Recognition.


Much like IER, both NER and EL have the objective of binding a natural language expression to a semantic identifier.

related work in NER and EL expect the proper name (explicit mention) of entities and assume the presence of noun phrases (Collins and Singer, 1999) (Bunescu and Pasca, 2006).


Since our solution to IER establishes a relationship between entity definitions and the input text, the tasks of paraphrase recognition (Barzilay and Elhadad, 2003) (Dolan et al., 2004) and textual entailment recognition (Giampiccolo et al., 2007) are related to our solution.

these tasks are fundamentally different in two aspects: 1) Both paraphrase recognition and textual entailment recogni-tion are defined at the sentence level, whereas text phrases considered for IER can exist as a sentence fragment or span across multiple sentences, and 2) The objective of IER is to find whether a given text phrase has a mention of an entity-as opposed to determining whether two sentences are similar or entail one another.


This particular type of questions resembles implicit entity mentions.

they assume that the questions are referring to some concept and the problem is to uncover which one, whereas the implicit entity mention problem requires us to first check whether a particular sentence/phrase has a mention of an entity at all.


Negation detection is traditionally separated from the entity recognition task because negation indicating terms can be recognized separately from the phrases that contain explicit mention of an entity.

implicit mention can involve an antonym that fuses the entity indication with negated sense  Figure 1: Components of the Proposed Solution (e.g., 'patient denies shortness of breath' vs 'patient is breathing comfortably').


The prepared data set is available at http://knoesis.org/ researchers/sujan/data-sets.html Since IER is a novel task, there are no baseline algorithms that can be directly applied such that it would yield a fair comparison with our algorithm.

we deem some of the related algorithms to have good potential applicability for this task.


Since the MCS showed low precision for the Tneg category in the previous experiment (Table 2), it is potentially introducing too much noise that the SVM is not able to linearly separate.

the similarity value calculated by our algorithm improves the SVM classifiers.


To build an unrestricted semantic role labeler, the first step is to develop a comprehensive proposition bank.

creating such a bank is a costly enterprise, which has only been achieved for a handful of languages.


building proposition banks is a costly enterprise and as a consequence of that, they only exist for a handful of languages such as English, Chinese, German, or Spanish.


(2009), we used an external resource of relational facts and we matched the entity pairs in the relations to a Swedish text corpus.

our approach substantially differs from theirs by the form of the external resource, which is a parsed corpus.


Since SRL extracts relations between a predicate and its arguments, it can be considered as a form of relation extraction which involves a deeper analysis.

the semantic units produced by classical semantic role labeling are still shallow, as they do not resolve coreference or disambiguate named entities.


Although this task has been addressed in literature, most of the publications report analyses based on the written text only, usually at the phrase or sentence level.

a written text is not merely a set of words or sentences.


The projectivity restriction itself has not been linguistically motivated to date.

an efficient exact algorithm for this restriction would pose a starting point for various relaxations of the definition (also known as mild non-projectivity) that reflect linguistic description of the data, as has been done for the Eisner algorithm (in for example (Bodirsky et al., 2005;Gómez-Rodríguez et al., 2011;Pitler et al., 2012;Pitler et al., 2013;Satta and Kuhlmann, 2013)).


Also, every outerplanar digraph corresponds to some projective digraph with a sentential ordering defined by node traversal in a fixed direction along its outer face.

given an outerplanar graph and some random sentential order, we of course do not necessarily have a projective digraph.


The authors are not aware of any specific theoretical linguistic evidence for the planarity or outerplanarity of semantic dependency DAGs.

we consider the three datasets from SemEval 2014 task 8 on Broad Coverage Semantic Dependency Parsing (Oepen et al., 2014), referred to as PAS, DM and PCEDT, following the packing conversion into DAGs described in , without the actual label packing or edge removal heuristic, finding that almost all DAGs are outerplanar (and therefore also planar).


Our semantic transition function is similar to these functions.

our method is more general than that of Milward.


Traditionally, the resolution of noun phrases (NPs) has been the focus of coreference research (Ng, 2010).

nPs are not the only participants in coreference, since verbal or clausal mentions can also take part in coreference relations.


Due to the tight coupling of multiple resolvers, a direct comparison with systems focusing on discourse deixis is hard.

their features are among the ones considered in this work.


Our TWOSTAGE system improves over NAIVE c by an additional 4% F1.

the scores remain low-partly because of the difficulty of the problem (especially the class imbalance), and partly because despite using a rich set of features, most of them focus on local context and ignore cues at the discourse level.


The outdegree of the combined graph P ER w + P DR w is highly correlated, showing that the interaction of entity connections and discourse relations is important for text coherence.

none of the outdegree measures in this table are significantly correlated with human readability rat-ings, confirming the intuition that outdegree only measures node connectivity in graphs and it is not enough to measure readability.


If readers have to return to one point in the text, they prefer to return to a sentence which is the core of the preceding sentences.

we should refrain of interpreting too much into these patterns.


A comparison with the results reported in previous work is difficult, due to the great discrepancies in the experimental settings (see the introduction).

our results clearly show that the aspectual class characterising verbal readings can be predicted with a reasonable precision on the basis of lexical-related information only.


This paper focuses on the issue of aspectual variability for the computational modelling of aspectual classes, by using a machine learning approach and a rich morpho-syntactic and semantic valency lexicon.

to previous work, where the aspectual value of corpus clauses is determined at the type (verb) level on the basis of features retrieved from the corpus, we make use of features retrieved from the lexicon in order to predict an aspectual value for each reading of a same verb (as they are delineated in this lexicon).


A foradverbial typically signals that the event is incomplete when it modifies a weak accomplishment; e.g., Peter filled the truck for one hour suggests that the filling event is not finished, see (Bott, 2010) a.o.

the same adverbial does not trigger this inference when it applies to a strong accomplishment or a (quasi)-achievement.


They found that on average metaphors were rated as being more emotional than non-metaphoric expressions.

that work did not compare paraphrase pairs that differed in just one word (metaphorically or literally used) and thus did not control for context.


A vast majority of work in sentiment analysis has focused on developing classifiers for valence prediction (Kiritchenko et al., 2014;Dong et al., 2014;Socher et al., 2013;, i.e., determining whether a piece of text expresses positive, negative, or neutral attitude.

there is a growing interest in detecting a wider range of emotions such as joy, sadness, optimism, etc.


We leave this as a topic for future work.

our results confirm the validity of our hypothesis with respect to metaphorical and literal lexical substitutes.


EL allows us to express a degree of uncertainty in the formulation of the converse, and this is appropriate to the extent that lexical glosses cannot be expected to provide complete, "airtight" definitions, but rather just the most important semantic content.

in this paper we limit ourselves to discussion of the "forward" version of gloss-derived axioms.


Figure 5 shows an example of the use of the new instance triple in mapping the EL formula for "I am very happy" into these representations.

this mapping does not relate the semantics of EL to AMR since the interpretation of the triples differ for AMR and EL formulas.


SRL systems (Täckström et al., 2015;Roth and Woodsend, 2014) have benefited NLP applications, and many approaches have been proposed to transfer semantic roles from English to other languages without further reliance on manual annotation (Kozhevnikov and Titov, 2013;Padó and Lapata, 2009).

event structures -both predicates and their arguments -are known to shift in the translation process, and this poor correspondence presents a bottleneck for the transference of semantic roles across languages.


They detect implicit semantic roles across documents and their antecedents from the prior context, again following cross-document links.

our work does not rely on semantic resources (SRL and lexical ontologies), but builds on parallel corpora enriched with dependencies and word alignments.


Each token of the role fillers in the output/input string is aligned to the token of the role fillers in the input/output string that has the maximum lexical translation probability.

to MEANT which measures lexical similarity using a monolingual context vector model, XMEANT instead substitutes simple crosslingual lexical translation probabilities.


In general, the RTE datasets are created semi-automatically and are often motivated by the scenarios found in the applications like question answering, relation extraction, infor-mation retrieval and summarization (Dagan et al., 2005;Dagan et al., 2013).

the semanticists are busy designing theories that account for the valid logical relations over natural language sentences.


They are also non-equivalent since their consequents are semantically weaker than their antecedents; this requires that after their application, the antecedent nodes are still reusable for further rule applications.

(∀ T ) is non-equivalent and γ; for instance, for any en-Efficeint theorem proving with LangPro becomes more challenging with multi-premised problems and monotonic GQs.


the mapping from a sentence to input structures is mediated by a dependency parser, a predicate-argument extractor and a verb lexicon covering the polar restric-tions -these components are language-dependent.


It would still occupy the posfor role, but since negated, it would no longer count as an entity that has received a positive, beneficial effect from the situation.

it would now be a victim, since it is denied help.


We define the concepts MyOpponent and MyProponent to capture the reader's perspective.

we found it much more convenient to use SWRL rules (Horrocks and Patel-Schneider, 2004) instead of pure OWL concepts (Horrocks and Patel-Schneider, 2011) to define the remaining relational inference layer.


When incorporating supervision, the more direct method of downstream sampling of the VerbNet class may be preferred to using a prior.

the verb senses are generated through a DPMM, and we do not have a gold-label assignment of VerbNet classes to each sense.


for evaluation, the authors only clustered senses of verbs in the evaluation set.


For instance, the Interactive Alignment Model (Pickering and Garrod, 2004) claims that priming mechanisms, which are an inherent feature of humans' cognitive architecture, lead to interpersonal coordination during dialogue.

communication Accommodation Theory (Shepard et al., 2001) focuses on the external factors influencing linguistic accommodation (e.g., the wish to build rapport) and argues that converging on linguistic patterns reduces the social distance between interlocutors, which results in speakers being viewed more favourably.


In the current setting, if w 2 = 0, we obtain the original measure where only the presence of a marker is recorded, without taking into account frequency and hence post length.

if w 1 = 0, only relative marker frequency is considered and no importance is given to the mere presence of a marker in a post.


As illustrated by this example, strongly agreeing responses often consist of short explicit expressions of agreement, with less potential for stylistic alignment.

disagreeing responses tend to be longer (as already observed by conversational analysts such as Pomerantz (1984)) and have therefore more chances to include stylistic markers matching the quoted post.


They greedily align content words between sentences by the similarity of their distributional vectors and then sum the similarity scores of aligned word pairs.

such greedily obtained alignment is not necessarily optimal.


GRAPHSEG performs competitively, outperforming all methods but (Fragkou et al., 2004) and domain-adapted versions of LDA-based models (Misra et al., 2009;Riedl and Biemann, 2012).

the approach by (Fragkou et al., 2004) uses the gold standard information -the average gold segment size -as input.


However, the approach by (Fragkou et al., 2004) uses the gold standard information -the average gold segment size -as input.

the LDA-based models adapt their topic models on parts of the Choi dataset itself.


These systems allow users to insert arbitrary text, which is then semiautomatically converted into pictographs.

they do not provide automatic translation aids based on linguistic knowledge to properly disambiguate lexical ambiguities, which can lead to erroneous translation (Vandeghinste, 2012).


The labeling of the latter can be automatically carried out by checking if a given passage contains the correct answer keyword or not.

this method is prone to generate passage labels, i.e., containing the answer keyword but not supporting it.


On the one hand, she confirmed Sandra's results that there is no semantic facilitation of any constituent in opaque compounds.

she found evidence for morphological complexity, independent of semantic transparency, and that both transparent and also partially opaque compounds (i.e., compounds with one transparent and one opaque constituent) produce semantic priming of their constituents.


Sentiment analysis tasks are formulated as determining whether a piece of text is positive, negative, or neutral, or determining from text the speaker's opinion and the target of the opinion (the entity towards which opinion is expressed).

in stance detection, systems are to determine favorability towards a given (prechosen) target of interest.


Some other feature sets that we experimented with, via cross-validation on the training set, included word embeddings trained on a generic Twitter corpus (not the domain corpus), the number of occurrences of each part-of-speech tag, the number of repeated sequences of exclamation or question marks, and the number of words with one character repeated more than two times (for example, yessss).

they did not improve results there, and so we did not include them for the test set experiments.


For instance, a chef and a fisherman may have different representations of the word fish (Wierzbicka, 1984).

the vocabularies and conceptual representations of individual people should be close enough that they can successfully communicate: this is ensured by the fact that many linguistic utterances are shared amongst a community.


Vector space models and η-normed-based similarity measures are notable examples of steps two and three, respectively (i.e., word space models or word embeddings).

as pointed out for instance by , the count-based models resulting from the steps two and three are not discriminative enough to achieve satisfactory results; instead, predictive models are required.


The PoP-constructed models converge faster than RI and count-based methods and for smaller contextwindows they outperform the count-based and RI methods with a large margin.

as the sizes of the windows grow, performances of these methods become more similar (but PoP still outperforms the others).


As a result, PPMI weights for the RIinduced vector elements are undefined.

to RI, the sum of values of vector elements in the PoP-constructed models is always greater than 0 (because the transformation is carried out by a projection matrix R of E(R) > 0).


For PPMI transformed PoP spaces, with increasing dimensions, the performance boosts and, furthermore, the variance in performance (i.e., the shaded areas) 15 gets smaller.

for the count-based PoP method without PPMI transformation (shown by the dashdotted lines) and with the number of non-zero elements fixed to 4, increasing m over 2000 decreases the performance.


In recent years, work on the Semantic Web has led to the publication of large scale datasets in the socalled Linked Data framework such as for instance DBPedia or Yago.

as shown in (Rector et al., 2004), the basic standards (e.g., RDF, OWL) established by the Semantic Web community for representing data and ontologies are difficult for human beings to use and understand.


Thus for instance, for the property PROGRAMMINGLANGUAGE, our model produces the lexicalisation "programming language for" which is clearly an L-lexicalisation that can be directly derived from the property name.

it also derives more context-sensitive E-lexicalisations such as "written in", "uses" and "based on" which are not lexically related to the property name but can be found by considering E-related candidate lexicalisations i.e., sentences such as "FastTacker Digit was written in Pascal" which contain entities that are arguments of the PROGRAMMINGLANGUAGE property.


Charles and Miller (1989) put forward the cooccurrence hypothesis that antonyms occur together in a sentence more often than chance.

non-antonymous semantically related words Antonym Pair not sufficient/insufficient sufficient/insufficient insignificant/negligible significant/negligible dishonest/lying honest/lying unusual/pretty strange usual/pretty strange Table 1: Examples of antonyms derived from PPDB paraphrases.


Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors.

it is not clear how much information such representations retain about the polarity of sentences.


These models have advantage over distributed representations as: i) they are specialised on a single task rather than built as general-purpose representations; ii) their recurrent nature allows to capture the sequential composition of polarity in a sentence.

since training these models requires large amounts of annotated data, resource scarcity in other languages hampers their portability.


Since cohyponyms are words that share a common hypernym (for example, salsa and tango are cohyponymys with respect to dance), hypernymy does not hold between them in any direction.

random examples (also used by Baroni et al.


This dataset consists of word pairs along with a pair of sentential contexts, with a label indicating the semantic relation between the two words in the given contexts.

since CONTEXT-PPDB only consists of 3700 sentence pairs, it provides only a smaller number of annotated examples per relation, making it difficult to train large supervised models on (we return to this dataset in Section 5).


Finally, note that the all-true baseline surprisingly does as well as the best context-aware model on this metric.

it cannot detect the direction of hypernymy (Section 6.3), and the structure of WHIC allows us to distinguish these two factors.


If the ϕ is adapted, the model will converge at a point which maximize the probability difference of the words between the initial θ d and θ c .

in the D-WDM model, there is no difference between the domain dictionary D d and the common dictionary D c except the parameter ϕ.


Because of the large input feature space, the only feasible way to train a model on those features is by using a linear classifier.


However, 14 Past work has found the SHR for sentiment intensity annotations for words, with 8 annotations per tuple, to be 0.98 (Kiritchenko et al., 2014).

here SHR is calculated from 3 annotations, for emotions, and from whole sentences.


There are many semantic frame resources, such as FrameNet (Baker et al., 1998), VerbNet (Schuler, 2006), Prop-Bank (Palmer et al., 2005) and Corpus Pattern Analysis (CPA) frames (Hanks, 2012).

most existing frame resources are manually created, which is time-consuming and expensive.


But TRNN does not know the word 'Elegans' in sentence 3 so it usually selects the most frequent frame seen in the training data.

in many cases, the unseen words can be captured by well trained word embeddings as the sentence 2 shows where 'ducks', 'chickens' and 'geese' are all unseen words.


Our target words must trigger some semantic parse, so our target words are actually target triggers.

the surrounding context words are those words that fill semantic roles.


Traditionally, such corpora are hand annotated for topic segments to serve as training material.

on large corpora such as the CXD corpus, this can be cumbersome and timeconsuming.


For example, an adjective acts as a function on the noun in an adjective-noun phrase, while a transitive verb acts as a binary function on its subject and object.

since the number of parameters in a tensor grows exponentially with the number of arguments of the function that it models, learning full tensors for predicates with many arguments is tedious to impractical (Grefenstette et al., 2012).


There are only 3 EDs describing the event, each of which uses the event verb "choose".

we find that "choose" is used in less than 10% of the event mentions in InScript.


For example, the WASH event in the TAKING A BATH scenario is often broken up into three mentions: wetting the hair, applying shampoo, and washing it again.

because there is only one event type for the three mentions, this sequence is never observed in DeScript.


−−→ man and − −−−− → woman could be expected to be reasonably similar distributionally, as they combine with many of the same verbs: both men and women sit, sleep, drink etc.

the same could not be said of words derived with prefixes that change part of speech.


FrameNet has inspired a direction in NLP research known as semantic role labelling (Gildea and Jurafsky, 2002;Màrquez et al., 2008) and frame-semantic parsing (Das et al., 2014), whose goal is to assign semantic roles to arguments of the verbs in a sentence.

these works point out the coverage limitations of the hand-constructed FrameNet database, suggesting that a data-driven frame acquisition method is needed to enable the integration of frame semantics into real-world NLP applications.


Frame semantics emphasizes the relation between our lexical semantic knowledge and our experience in the world, suggesting that semantic frames are not merely a linguistic construct but also a result of our sensory-motor and perceptual experience.

frame semantic approaches in NLP typically rely on textual data.


Since this approach acquires paraphrases from descriptions of the same event, it is potentially more accurate than methods that acquire paraphrases from the entire corpus or translation phrase table.

there is currently no paraphrase resource acquired in this approach.


If questions can be answered by examining local contexts only, then the use of a knowledge-base is perhaps unnecessary.

to the best of our knowledge, only models that utilize the KB have been evaluated on common semantic parsing benchmarks.


Our model WEBQA performs QA using web snippets, similar to traditional QA systems like MUL-DER (Kwok et al., 2001) and AskMSR (Brill et al., 2002).

it it enjoys the advances in commerical search engines of the last decade, and uses a simple log-linear model, which has become standard in Natural Language Processing.


(2014) analyzed a semantic parsing benchmark with a simple QA system.

they employed a semantic parser that is limited to applying a single binary relation on a single entity, while we develop a QA system that does not use the target KB at all.


Formally, the global score θ e k of an event e k is defined as: meaning that the degree of semantic coherence of an event is given by the product of the partial thematic fit scores between all its components.

8 the σ e k score weights the salience of the unified event e k by combining the weights of e i and e j into a new weight assigned to e k .


Most of the semantics literature on prepositions has revolved around how they categorize space and time (e.g., Herskovits, 1986;Verkuyl and Zwarts, 1992;Bowerman and Choi, 2001).

there have been a couple of lines of work addressing preposition semantics broadly.


We do not discount the possibility that such a metaphor can be cognitively active in speakers using temporal adpositions; in fact, there is considerable evidence that time-as-space metaphors are cross-linguistically pervasive and productive (Lakoff and Johnson, 1980;Núñez and Sweetser, 2006;Casasanto and Boroditsky, 2008).

we do not see much practical benefit to annotating temporal at or topical about as spatial.


Annotators are generally capable of interpreting meaning in a given context.

it might be difficult to train annotators to develop intuitions about adposition functions, which reflect prototypical meanings contributed by the lexical item that may not be literally applicable.


Suggestions include rules for grinding (armadillo all over the road), portioning (Order me a beer), sorting (eight beers on tap), evaluating (too much car for the average driver) and others.

it has seemed clear to most theorists that this sort of strategy wouldn't be able to account for all the varied ways that +MASS and +COUNT senses of a given noun might be related.


For many RE applications, such as knowledge base population, patterns are not only relevant if they express the target relation explicitly, but also if they extract facts from which the target relation can be inferred.

for example, all patterns below can be utilized for extracting pairs of people who are or were involved in a marriage relation: P1: PERSON 1 <marry> PERSON 2 1 P2: PERSON 1 <be widow of> PERSON 2 P3: PERSON 1 <divorce from> PERSON 2 only pattern P1 expresses the target relation explicitly.


For example, clustering can help us identify pattern P4 below as being semantically related to patterns P1 to P3 in section 1.

it falls short of expressing that two entities linked by patterns P1 to P3 are mentions of the marriage relation, whereas this is not necessarily true of entities linked by pattern P4.


Several studies have shown that some manifestations of schizophrenia (e.g., the negative symptoms that include blunting of speech prosody, as well as the disorganization symptoms that lead to disordered language) can be understood from the perspective of linguistics.

schizophrenia research has not kept pace with technologies in computational linguistics, especially in semantics and pragmatics.


Research in question answering has traditionally focused on learning from question-answer pairs (Burger et al., 2001).

it is well-established in the educational psychology literature (Allington and Cunningham, 2010;Felder et al., 2000) that children tend to learn better and faster from concrete illustrations and demonstrations.


This kind of deductive reasoning is used in explanations in textbooks.

to GEOS which uses supervised learning, our approach learns to solve geometry problems by interpreting natural language demonstrations of the solution.


Learning from natural instructions allows human teachers to interact with an automated learner using natural instructions, allowing the teacher to communicate the domain expertise to the learner via natural language.

this work was evaluated on a very simple Freecell game with a very small number of concepts (3).


However, this work was evaluated on a very simple Freecell game with a very small number of concepts (3).

our model is evaluated on a real task of solving SAT style geometry problems.


The thematic fit judgements from the tasks discussed in section 6 only contain ratings of the fit between the predicate and one role-filler.

other event participants contained in a clause can affect human expectations of the upcoming role-fillers.


We suspect that the word salience model has the best performance in the OWNs datasets because they are closest to the training data.

it outperforms the other models in other datasets such as images, and student-answers which talks about the generalisability of the model.


Moreover, perceived psycholinguistic properties of words such as concreteness (how "palpable" the object the word refers to) and imageability (the intensity with which a word arouses images) have been successfully predicted using word embeddings (Turney et al., 2011;Paetzold and Specia, 2016).

for example, (Turney et al., 2011) used the cosine similarity between word embeddings obtained via La-  tent Semantic Analysis (LSA) (Deerwester et al., 1990) to predict the concreteness and imageability prior work studying the relationship between human reading patterns using eye-tracking devices show that there exist a high positive correlation between word salience and reading times (Dziemianko et al., 2013;Hahn and Keller, 2016).


since we performed 438 assessments (219 submissions evaluated for biases in both gender and race), we applied Bonferroni correction.


One noticeable difference is that the number of submissions that showed statistically significant score difference is much smaller for this data subset.

the total number of comparisons on the subset (44) is much smaller than the total number of comparisons on the full set (1,584), which makes the statistical test less powerful.


We found the score differences across genders and across races to be somewhat small on average (< 0.03, which is 3% of the 0 to 1 score range).

for some systems the score differences reached as high as 0.34 (34%).


(2015), where a CCG parser is adapted to produce shallow semantic dependency graphs.

the meaning representations employed here are abstract and do not directly refer to the sentence under analysis.


In addition to the AMR labels, a source labelling function is employed, as defined in Section 2.3.

source labels used in CCG/AMR sgraphs take one of two forms: • r, i with r ∈ {root, ∅} and i ∈ N ∪ {∅}, which marks a node as root if r = root, and assigns it an index i if i ∈ N. we disallow ∅, ∅ as a source label.


• combine: Take the union of both aligners' outputs.

if a node has been aligned to different tokens by the two aligners, drop alignments for this node altogether.


In general, prepositions dominate among the most ambiguous items, because they occur frequently, tend not to have any alignments, and also have a high degree of legitimate polysemy.

high-frequency words specific to the corpus also appear, such as government or security.


The nouns in the context of concrete noun targets are also very concrete as shown by the high red bar at concreteness 4.5-5.

verbs and adjectives show a similar pattern to Figure 1a: a greater distribution with low concreteness scores.


Contexts of abstract verbs are, on average, also abstract, regardless of their POS.

the verbs and adjectives in the contexts of concrete verb targets are mainly abstract, while the nouns are mainly concrete.


More specifically, our first hypothesis is confirmed, on the one hand, by the contextual distribution of concrete target nouns, due to the fact that they frequently appear with other concrete nouns.

it is rejected by the contextual ratio of abstract nouns as they primarily co-occur with other abstract nouns.


Another evidence in favour of our hypothesis comes from the nouns in the context of concrete verbs and adjectives that are mainly concrete.

concrete and abstract nouns, verbs and adjectives elicit the same contextual pattern regarding context verbs and adjectives.


(2014, p. 906) describe concrete words in a similar way: "some words refer to things or actions in reality, which you can experience directly through one of the five senses".

our study is aligned more with recent theories claiming a representational pluralism that includes both perceptual and non-perceptual features (Dove, 2009).


Nowadays, social media have become a platform where people can easily express their opinions and emotions about any topic such as politics, movies, music, electronic products and many others.

politicians, companies, and businesses are interested in analyzing automatically people's opinions and emotions.


So it is not surprising that cross-language extensions of such models (crosslanguage word embeddings) rapidly gained popularity in the NLP community (Vulić and Moens, 2013), proving their effectiveness in certain crosslanguage NLP tasks (Upadhyay et al., 2016).

the problem of proper evaluation of any type of word embeddings still remains open.


One of the ways of avoiding this problem is to exclude relational nouns which are words with non-zero valency (Koptjevskaja-Tamm et al., 2015) from the dataset, so it should consist only of zero valency nouns that are more properly linked with real world objects.

the distinction of words on relational and non-relational ones is fuzzy, and such assessments could be very subjective (also, since verbs are usually highly relational, they should not be used in cross-language evaluation).


For instance, our analysis revealed that light skin tones are more widely used than dark ones and their usage is different in many cases.

incidentally the dark raised fist emoji (i.e. )